Identification of Parametric Ground Motion Random Fields from Spatially Recorded Seismic Data

Creators: Zerva, A.; Beck, J. L.

Abstract

A methodology for the identification of random field models for the description of seismic ground motions recorded over extended areas is presented. The approach (i) embeds seismic ground motion parameter estimation in a probabilistic framework, and (ii) identifies simultaneously point and spatial models for the seismic ground motion random field from either smoothed or unsmoothed non-parametric spectral estimates. It is shown that parametric coherency estimates identified by means of this approach are stable and insensitive to the amount of smoothing performed on the empirical data. The approach and its findings have a significant impact on the evaluation of parametric ground motion models for the seismic response evaluation of lifelines.

Additional Information

Copyright © 2003 John Wiley & Sons, Ltd. Received 14 January 2001, Revised 17 January 2002 and 5 August 2002, Accepted 14 August 2002. Contract=grant sponsor: USA NSF; contract=grant number: CMS-9725567, POWRE-CMS-9870509. AZ was supported by USA NSF grants CMS-9725567 and POWRE-CMS-9870509; the Bayesian updating methodology was incorporated in the approach while she was stationed at the California Institute of Technology. The NSF support and Caltech's hospitality are gratefully acknowledged. The writers want to thank two anonymous reviewers for their valuable comments that have contributed to the improvement of the original manuscript, and, especially, the suggestion that the homoscedastic form for the prediction-error model be used. This paper is dedicated to the memory of Professor Donald E. Hudson of Caltech, a pioneer in the area of Earthquake Engineering.

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Identification of Parametric Ground Motion Random Fields from Spatially Recorded Seismic Data

Abstract

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